subdiagnosis <- readr::read_tsv(
file.path("..", "..", "..", "data", "current", params$scpca_project_id, "single_cell_metadata.tsv"),
show_col_types = FALSE
) |>
dplyr::filter(scpca_sample_id == params$sample_id) |>
dplyr::pull(subdiagnosis)This notebook explores using CopyKAT
or infercnv
to estimate tumor and normal cells in SCPCS000184 from SCPCP000006. This
sample has a(n) Anaplastic subdiagnosis.
CopyKAT was run using the 05_copyKAT.R
script with and without a normal reference, using 2 different methods to
calculate the distance, namely euclidean or
spearman.
infercnv was run using the 06_inferCNV.R
script with and without a normal reference. We also tested the impact of
the subselection of normal cells using either immune, and/or endothelial
cells as healthy reference.
In this notebook, we just want to compare the heatmaps of CNV profiles, and evaluate how comparable are the methods and how snesible they are to key parameters such as choice of distance or choice of healthy reference.
The input for this notebook are the results of
05_copyKAT.R and 06_inferCNV.R
Below we look at the heatmaps produced by CopyKAT.
distance = euclideandistance = spearmandistance = euclideandistance = spearman# record the versions of the packages used in this analysis and other environment information
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Vienna
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] infercnv_1.20.0 ggalluvial_0.12.5 org.Hs.eg.db_3.19.1 AnnotationDbi_1.66.0 IRanges_2.38.1 S4Vectors_0.42.1 Biobase_2.64.0
## [8] BiocGenerics_0.50.0 clusterProfiler_4.12.6 enrichplot_1.24.4 msigdbr_7.5.1 patchwork_1.2.0 lubridate_1.9.3 forcats_1.0.0
## [15] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1
## [22] tidyverse_2.0.0 SCpubr_2.0.2 sctransform_0.4.1 Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4 optparse_1.7.5
## [29] Matrix_1.7-0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-8 fs_1.6.4 matrixStats_1.3.0 spatstat.sparse_3.1-0 httr_1.4.7 RColorBrewer_1.1-3
## [7] doParallel_1.0.17 tools_4.4.1 DT_0.33 utf8_1.2.4 R6_2.5.1 lazyeval_0.2.2
## [13] uwot_0.2.2 withr_3.0.1 gridExtra_2.3 parallelDist_0.2.6 progressr_0.14.0 argparse_2.2.3
## [19] cli_3.6.3 formatR_1.14 spatstat.explore_3.3-2 fastDummies_1.7.4 scatterpie_0.2.4 sandwich_3.1-1
## [25] sass_0.4.9 mvtnorm_1.3-1 spatstat.data_3.1-2 ggridges_0.5.6 pbapply_1.7-2 yulab.utils_0.1.7
## [31] gson_0.1.0 DOSE_3.30.5 R.utils_2.12.3 parallelly_1.38.0 limma_3.60.4 rstudioapi_0.16.0
## [37] RSQLite_2.3.7 generics_0.1.3 gridGraphics_0.5-1 vroom_1.6.5 gtools_3.9.5 ica_1.0-3
## [43] spatstat.random_3.3-1 GO.db_3.19.1 futile.logger_1.4.3 fansi_1.0.6 abind_1.4-5 R.methodsS3_1.8.2
## [49] lifecycle_1.0.4 yaml_2.3.10 edgeR_4.2.1 multcomp_1.4-26 SummarizedExperiment_1.34.0 gplots_3.1.3.1
## [55] SparseArray_1.4.8 qvalue_2.36.0 Rtsne_0.17 grid_4.4.1 blob_1.2.4 promises_1.3.0
## [61] crayon_1.5.3 miniUI_0.1.1.1 lattice_0.22-6 cowplot_1.1.3 KEGGREST_1.44.1 pillar_1.9.0
## [67] knitr_1.48 GenomicRanges_1.56.1 fgsea_1.30.0 future.apply_1.11.2 codetools_0.2-20 fastmatch_1.1-4
## [73] leiden_0.4.3.1 glue_1.7.0 ggfun_0.1.6 spatstat.univar_3.0-0 data.table_1.16.0 vctrs_0.6.5
## [79] png_0.1-8 treeio_1.28.0 spam_2.10-0 gtable_0.3.5 cachem_1.1.0 xfun_0.47
## [85] S4Arrays_1.4.1 mime_0.12 libcoin_1.0-10 tidygraph_1.3.1 coda_0.19-4.1 survival_3.7-0
## [91] SingleCellExperiment_1.26.0 iterators_1.0.14 statmod_1.5.0 TH.data_1.1-2 fitdistrplus_1.2-1 ROCR_1.0-11
## [97] nlme_3.1-166 ggtree_3.12.0 bit64_4.0.5 RcppAnnoy_0.0.22 rprojroot_2.0.4 GenomeInfoDb_1.40.1
## [103] bslib_0.8.0 irlba_2.3.5.1 KernSmooth_2.23-24 colorspace_2.1-1 DBI_1.2.3 tidyselect_1.2.1
## [109] bit_4.0.5 compiler_4.4.1 httr2_1.0.3 DelayedArray_0.30.1 plotly_4.10.4 shadowtext_0.1.4
## [115] caTools_1.18.2 scales_1.3.0 lmtest_0.9-40 rappdirs_0.3.3 digest_0.6.37 goftest_1.2-3
## [121] spatstat.utils_3.1-0 rmarkdown_2.28 XVector_0.44.0 htmltools_0.5.8.1 pkgconfig_2.0.3 MatrixGenerics_1.16.0
## [127] fastmap_1.2.0 rlang_1.1.4 htmlwidgets_1.6.4 UCSC.utils_1.0.0 shiny_1.9.1 jquerylib_0.1.4
## [133] farver_2.1.2 zoo_1.8-12 jsonlite_1.8.8 BiocParallel_1.38.0 GOSemSim_2.30.2 R.oo_1.26.0
## [139] magrittr_2.0.3 modeltools_0.2-23 GenomeInfoDbData_1.2.12 ggplotify_0.1.2 dotCall64_1.1-1 munsell_0.5.1
## [145] Rcpp_1.0.13 ape_5.8 babelgene_22.9 viridis_0.6.5 reticulate_1.38.0 stringi_1.8.4
## [151] ggraph_2.2.1 zlibbioc_1.50.0 MASS_7.3-61 plyr_1.8.9 parallel_4.4.1 listenv_0.9.1
## [157] ggrepel_0.9.5 deldir_2.0-4 Biostrings_2.72.1 graphlayouts_1.1.1 splines_4.4.1 tensor_1.5
## [163] hms_1.1.3 locfit_1.5-9.10 fastcluster_1.2.6 igraph_2.0.3 spatstat.geom_3.3-2 RcppHNSW_0.6.0
## [169] reshape2_1.4.4 futile.options_1.0.1 evaluate_0.24.0 RcppParallel_5.1.9 lambda.r_1.2.4 phyclust_0.1-34
## [175] tzdb_0.4.0 foreach_1.5.2 tweenr_2.0.3 httpuv_1.6.15 RANN_2.6.2 getopt_1.20.4
## [181] polyclip_1.10-7 future_1.34.0 scattermore_1.2 ggforce_0.4.2 coin_1.4-3 xtable_1.8-4
## [187] RSpectra_0.16-2 tidytree_0.4.6 later_1.3.2 rjags_4-16 viridisLite_0.4.2 aplot_0.2.3
## [193] memoise_2.0.1 cluster_2.1.6 timechange_0.3.0 globals_0.16.3